Data Governance, Key for Self-service BI

In an age where organizations need the latest and most accurate data to make business decisions, self-service business Intelligence (BI) tools must come under the purview of a data governance plan. With the emergence of data as a key component in the decision-making process, data governance has come to play a crucial role in ensuring successful deployment of BI tools for self-service analytics. An effective data governance plan requires a proper balance between self-service analysis and securing sensitive business information.

As enterprises seek to observe and comprehend their data better, there is an unprecedented need for optimal infrastructure that enables a robust data governance strategy. To gain better insights from their data that would contribute to reduced costs of operations and enhance business outcomes, enterprises must consider a mix of best practices while developing a data governance plan.

Focus on Team Building

Before embarking on a long-drawn data governance project, it is advisable for organizations to assemble a project team that can carry out a reconnaissance of their current data analytics use. This exercise will enable an enterprise to define a concrete process that would lead up to the implementation of a full-fledged self-service data analytics methodology. The presence of IT and management leadership and even a stakeholder in the team will help them effectively deduce a balanced view of the enterprise's requirements for data governance and self-service.

Enterprises have the option of hiring full-time data stewards or delegating the data stewardship responsibilities to existing employees. By integrating data stewardship processes with their corporate culture, organizations can steer a robust data governance program that also fosters internal adoption and regulatory compliance.

Don’t Ignore Data Quality

While organizations look forward to build a data governance framework through the formation of data committees and stewardships, data quality is an aspect that they cannot afford to ignore. With humongous volumes of customer data pouring, enterprises must realize that all this valuable data is prone to leakage through security cracks. Investing in enterprise-grade data quality tools is a must for IT-intensive organizations that plan to implement a data governance framework within their IT infrastructure. For businesses that are serious about enforcing data governance throughout their enterprise, the safest bet lies in hiring a Chief Data Officer (CDO) who would be in charge of monitoring the data governance policies while governing the overall quality of information.

Ensure Data Security and Compliance

The self-service BI tools are modeled on the premise of allowing users to have unrestricted access to data and this often creates vulnerabilities in the form of an observable security gap. The solution to this anomaly lies in enforcing multiple layers of governance which also secures the data to prevent misuse and unauthorized alteration. The task of regulating access to ensure data security is a critical issue that an effective data governance strategy addresses.

Guidelines for Social Media and the Cloud

The proliferation of social media platforms and mobile devices has necessitated the need for enforcement of data governance policies on these networks too. To prevent data governance policy violations on mobile networks, enterprises must ensure that their data stewardship teams actively monitor these networks too. With Cloud BI and Analytics climbing the popularity charts, the need for data management for cloud platforms must be taken care of by implementing enterprise-grade data quality tools.

While deciding on the optimal data governance model, organizations must evaluate the objectives that they are trying achieve through it. This will validate the enterprise’s data governance plan before the stakeholders and help the data governance management team in chalking out a long-term data governance program.